Abstract: In There is provided a computer-implemented method for assessing an injection performed on a subject using a syringe provided with a needle, the method comprising: processing a sequence of images taken of the injection for: determining an insertion angle of the needle relative to the subject; and determining a depth of insertion of the needle within the subject; determining one of a speed of injection and a duration of injection; and outputting an indication of the insertion angle, the depth of insertion and the one of the speed of injection and the duration of injection.

Abstract: A training platform, a method and a computer-readable medium for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Automated machine learning models, trained on a dataset of labelled training images associated with different imaging device positions, are used. The one or more automated machine learning models are used to process an image resulting from a user positioning an imaging device at various imaging device positions relative to a training manikin, a human or an animal, to determine whether the generated image corresponds to a predefined view required for the analysis of the organ features shown therein. An output indicative of whether the generated image corresponds to the predefined view expected for organ analysis and measurements is provided.

Abstract: A non-transitory computer-readable medium, a system and a method are provided for identifying a grip technique being applied to a face mask. The mask is for sealingly engaging a face along a periphery around the nose and mouth. The method comprises the steps of comparing pressure data indicative of a pressure distribution applied along the perimeter with distribution patterns distinctive of different grip techniques; recognizing or detecting which of the grip techniques is being applied on the basis of an outcome of the comparing; and providing an indication of the recognized grip technique to a user interface.

Abstract: A training platform, a method and a computer-readable medium for evaluating users in capturing images of an internal anatomical region for the analysis of organs. Automated machine learning models, trained on a dataset of labelled training images associated with different imaging device positions, are used. The one or more automated machine learning models are used to process an image resulting from a user positioning an imaging device at various imaging device positions relative to a training manikin, a human or an animal, to determine whether the generated image corresponds to a predefined view required for the analysis of the organ features shown therein. An output indicative of whether the generated image corresponds to the predefined view expected for organ analysis and measurements is provided.

Abstract: A method for generating sound within a predetermined environment, the method comprising: emitting a first audio signal from a first location; and concurrently emitting a second audio signal from a second location, wherein: the first location and second location are distinct within the environment; the first audio signal and second audio signal have the same frequency; and the first audio signal and second audio signal have a phase difference that varies as a function of time to limit the time-averaged interference fluctuation across the environment.

Abstract: A method for generating sound within a predetermined environment, the method comprising: emitting a first audio signal from a first location; and concurrently emitting a second audio signal from a second location, wherein: the first location and second location are distinct within the environment; the first audio signal and second audio signal have the same frequency; and the first audio signal and second audio signal have a phase difference that varies as a function of time to limit the time-averaged interference fluctuation across the environment.

Abstract: A method for determining an actual position of a microphone, comprising: sequentially emitting a first sound signal via a first speaker positioned and a second sound signal via a second speaker positioned; measuring a first elapsed time between the emission of the first sound signal and a detection of the first sound signal by the microphone, and a second elapsed time between the emission of the second sound signal and a detection of the second sound signal by the microphone; determining a first distance between the first speaker and the microphone using the first elapsed time, and a second distance between the second speaker and the microphone using the second elapsed time; determining the actual position of the microphone using the first and second distances and the positions of the first and second speaker; and outputting the actual position.

Abstract: A method for determining an actual position of a microphone, comprising: sequentially emitting a first sound signal via a first speaker positioned and a second sound signal via a second speaker positioned; measuring a first elapsed time between the emission of the first sound signal and a detection of the first sound signal by the microphone, and a second elapsed time between the emission of the second sound signal and a detection of the second sound signal by the microphone; determining a first distance between the first speaker and the microphone using the first elapsed time, and a second distance between the second speaker and the microphone using the second elapsed time; determining the actual position of the microphone using the first and second distances and the positions of the first and second speaker; and outputting the actual position.